1. Version Control: Init the git repo
2. Dependencies:Can reuse existing environment: conda activate analytics-training-samples
3. Data: Set-up dvc
   • Create folder data/01_raw: Put the data there
   • dvc init
   • Set up remote storage: dvc remote add -d storage gs://dvc-data-storage (separate steps to set-up Google Storage and connect local machine to GCP)
   • Turn on autostaging: dvc config core.autostage true
   • dvc add data/01_raw/online_retail.xlsx
   • dvc push

Convert the notebook to script

1. Example notebook: notebooks/Original_OnlineRetail_Cohort.ipynb
2. Make a copy: notebooks/Original_OnlineRetail_Cohort-Copy1.ipynb
3. On Jupyter notebook:
   • Cell -> All Output -> Clear
   • Run All (To make sure that all cells are in right sequence)
   • Check all results
4. On Terminal, run:

jupyter nbconvert notebooks/Original_OnlineRetail_Cohort-Copy1.ipynb --to python

5. Check the script output, remove all print/notebook statements: print(...), df.head(), df.describe(...), etc.

Read through the code, sketch out the flow, list any code smells

1. Sketch out the flow
   • Ingest & process data
   • Aggregate to have the data input for plotting
   • Plotting the reports
2. List code smells & Clean code enhancements
3. Leave # TODO comments for things to change

Convert codes into DRY functions, write tests, and create local module (which could be imported to notebooks)

1. Create the src folder to keep all source codes
2. Create tests folder to keep tests for source codes
3. Write/Test follows the Test-driven Development (TDD) approach
   • tests/test_*.py: Write the test first, then write the code
   • pytest: Run the test, make sure it fails
   • Write the code, make sure it passes
   • Refactor the code, make sure it still passes
4. Structures within source code:
   • src/: All source codes
     • __init__.py: Empty file to make src a module
     • utils.py: Utility functions
     • ingest.py: All functions to process/clean data
     • process.py: All functions to aggregate data
     • plot.py: All functions to plot data
   • tests/: All tests
     • __init__.py: Empty file to make tests a module
     • test_utils.py: Utility functions for tests
     • test_ingest.py: Test for ingest.py
     • test_process.py: Test for process.py
     • test_plot.py: Test for plot.py
   • Within each test file:
     • Import the functions from local modules (src folder)
     • Tests for each function could be organized by Class with each test cases as methods (naming corresponding to the function name)
5. Write the code until passing the tests
6. Reformat with black
7. Commit to git
8. Pull request to merge to main branch

We refactor codes from the Original noteboks as local modules, and import them to the new notebooks. This will enforce the TDD approach, and reusable of functions across notebooks.

If you reuse local module a lot, consider to convert them to proper Python pkg with pip manager

See: notebooks/Refactored_OnlineRetail_Cohort.ipynb